preprocessing.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""Preprocessing.

This module contains functions for various preprocessing steps provided by `DARIAH-DE`_.

.. _DARIAH-DE:
    https://de.dariah.eu
    https://github.com/DARIAH-DE
"""

__author__ = "DARIAH-DE"
__authors__ = "Steffen Pielstroem, Philip Duerholt, Sina Bock, Severin Simmler"
__email__ = "pielstroem@biozentrum.uni-wuerzburg.de"

import glob
import os
from collections import Counter, defaultdict
import csv
import logging
from lxml import etree
import numpy as np
import pandas as pd
import regex
from itertools import chain


log = logging.getLogger('preprocessing')
log.addHandler(logging.NullHandler())
logging.basicConfig(level=logging.INFO,
                    format='%(levelname)s %(name)s: %(message)s')

regular_expression = r'\p{Letter}+\p{Punctuation}?\p{Letter}+'


def create_document_list(path, ext='txt'):
    """Creates a list of files with their full path.

    Description:
        Consider you have a collection of text files in one directory and wish to
        read all files at once, use this function to create a list of their full paths.
        Use the list as argument for the functions `read_from_txt()`, `read_from_tei()`
        or `read_from_csv()` to read the files.
        You have to specify the file format, which defaults to plain text.

    Args:
        path (str): Path to folder, e.g. '/tmp/corpus'.
        ext (str): File extension, e.g. 'csv'. Defaults to 'txt'.

    Returns:
        List of files with full path.

    Example:
        >>> doclist = create_document_list('corpus_txt')
        >>> 'corpus_txt/Doyle_AScandalinBohemia.txt' in doclist
        True
    """
    log.info("Creating document list from %s files ...", ext.upper())
    pattern = os.path.join(path, '*.' + ext)
    doclist = glob.glob(pattern)
    if not doclist:
        raise FileNotFoundError(
            "The pattern %s does not match any files.", pattern)
    return doclist


def read_from_txt(doclist):
    """Opens TXT files using file paths.

    Description:
        With this function you can read plain text files. Commit a list of
        full paths or one single path as an argument.
        Use the function `create_document_list()` to create a list of your text
        files.

    Args:
        doclist Union(list[str], str): List of all documents in the corpus
            or single path to TXT file.

    Yields:
        Document.

    Todo:
        * Separate metadata (author, header)?

    Example:
        >>> list(read_from_txt('corpus_txt/Doyle_AScandalinBohemia.txt'))[0][:20]
        'A SCANDAL IN BOHEMIA'
    """
    log.info("Accessing TXT documents ...")
    if isinstance(doclist, str):
        with open(doclist, 'r', encoding='utf-8') as f:
            yield f.read()
    elif isinstance(doclist, list):
        for file in doclist:
            with open(file, 'r', encoding='utf-8') as f:
                yield f.read()


def read_from_tei(doclist):
    """Opens TEI XML files using file paths.

    Description:
        With this function you can read TEI encoded XML files. Commit a list of
        full paths or one single path as an argument.
        Use the function `create_document_list()` to create a list of your XML
        files.

    Args:
        doclist Union(list[str], str): List of all documents in the corpus
            or single path to TEI XML file.

    Yields:
        Document.

    Todo:
        * Seperate metadata (author, header)?

    Example:
        >>> list(read_from_tei('corpus_tei/Schnitzler_Amerika.xml'))[0][142:159]
        'Arthur Schnitzler'
    """
    log.info("Accessing TEI XML documents ...")
    if not isinstance(doclist, list):
        doclist = [doclist]
    ns = dict(tei='http://www.tei-c.org/ns/1.0')
    for file in doclist:
        tree = etree.parse(file)
        text_el = tree.xpath('//tei:text', namespaces=ns)[0]
        yield "".join(text_el.xpath('.//text()'))


def read_from_csv(doclist, columns=['ParagraphId', 'TokenId', 'Lemma', 'CPOS', 'NamedEntity']):
    """Opens CSV files using file paths.

    Description:
        With this function you can read CSV files generated by `DARIAH-DKPro-Wrapper`_,
        a tool for natural language processing. Commit a list of full paths or
        one single path as an argument. You also have the ability to select certain
        columns.
        Use the function `create_document_list()` to create a list of your CSV
        files.
        .. _DARIAH-DKPro-Wrapper:
            https://github.com/DARIAH-DE/DARIAH-DKPro-Wrapper

    Args:
        doclist Union(list[str], str): List of all documents in the corpus
            or single path to CSV file.
        columns (list[str]): List of CSV column names.
            Defaults to '['ParagraphId', 'TokenId', 'Lemma', 'CPOS', 'NamedEntity']'.

    Yields:
        Document.

    Todo:
        * Seperate metadata (author, header)?

    Example:
        >>> list(read_from_csv('corpus_csv/Doyle_AScandalinBohemia.txt.csv'))[0][:4] # doctest: +NORMALIZE_WHITESPACE
                   ParagraphId  TokenId    Lemma CPOS NamedEntity
        0            0        0        a  ART           _
        1            0        1  scandal   NP           _
        2            0        2       in   PP           _
        3            0        3  bohemia   NP           _
    """
    log.info("Accessing CSV documents ...")
    if isinstance(doclist, str):
        doclist = [doclist]
    for file in doclist:
        df = pd.read_csv(file, sep='\t', quoting=csv.QUOTE_NONE)
        yield df[columns]


def get_labels(doclist):
    """Creates a list of document labels.

    Description:
        Consider you have a list of text files and wish to access their raw name
        without any extensions or parts of the path. With this function you
        can save document labels in a list.
        Use the function `create_document_list()` to create a list of your text
        files.

    Args:
        doclist (list[str]): List of file paths.

    Yields:
        Document labels.

    Example:
        >>> list(get_labels(['corpus_txt/author_title.txt']))
        ['author_title']
    """
    log.info("Creating document labels ...")
    for doc in doclist:
        label, ext = os.path.splitext(os.path.basename(doc))
        yield label


def split_paragraphs(doc_txt, sep=regex.compile('\n')):
    """Splits the given document by paragraphs.

    Description:
        With this function you can split a document by paragraphs. You also have
        the ability to select a certain regular expression to split the document.
        Use the functions `read_from_txt()`, `read_from_tei()` or `read_from_csv()`
        to read your text files.

    Args:
        doc_txt (str): Document text.
        sep (regex.Regex): Separator indicating a paragraph.

    Returns:
        List of paragraphs.

    Example:
        >>> split_paragraphs("This test contains \\n paragraphs.")
        ['This test contains ', ' paragraphs.']
    """
    if not hasattr(sep, 'match'):
        sep = regex.compile(sep)
    return sep.split(doc_txt)


def segment_fuzzy(document, segment_size=5000, tolerance=0.05):
    """Segments a document, tolerating existing chunks (like paragraphs).

    Description:
        Consider you have a document. You wish to split the document into
        segments of about 1000 tokens, but you prefer to keep paragraphs together
        if this does not increase or decrease the token size by more than 5%.

    Args:
        document: The document to process. This is an Iterable of chunks, each
            of which is an iterable of tokens.
        segment_size (int): The target length of each segment in tokens.
        tolerance (Number): How much may the actual segment size differ from
            the segment_size? If 0 < tolerance < 1, this is interpreted as a
            fraction of the segment_size, otherwise it is interpreted as an
            absolute number. If tolerance < 0, chunks are never split apart.

    Yields:
        Segments. Each segment is a list of chunks, each chunk is a list of
        tokens.

    Example:
        >>> list(segment_fuzzy([['This', 'test', 'is', 'very', 'clear'],
        ...                     ['and', 'contains', 'chunks']], 2)) # doctest: +NORMALIZE_WHITESPACE
        [[['This', 'test']],
        [['is', 'very']],
        [['clear'], ['and']],
        [['contains', 'chunks']]]
    """
    if tolerance > 0 and tolerance < 1:
        tolerance = round(segment_size * tolerance)

    current_segment = []
    current_size = 0
    carry = None
    doc_iter = iter(document)

    try:
        while True:
            chunk = list(carry if carry else next(doc_iter))
            carry = None
            current_segment.append(chunk)
            current_size += len(chunk)

            if current_size >= segment_size:
                too_long = current_size - segment_size
                too_short = segment_size - (current_size - len(chunk))

                if tolerance >= 0 and min(too_long, too_short) > tolerance:
                    chunk_part0 = chunk[:-too_long]
                    carry = chunk[-too_long:]
                    current_segment[-1] = chunk_part0
                elif too_long >= too_short:
                    carry = current_segment.pop()
                yield current_segment
                current_segment = []
                current_size = 0
    except StopIteration:
        pass

    # handle leftovers
    if current_segment:
        yield current_segment


def segment(document, segment_size=1000, tolerance=0, chunker=None,
            tokenizer=None, flatten_chunks=False, materialize=False):
    """Segments a document into segments of about `segment_size` tokens, respecting existing chunks.

    Description:
        Consider you have a document. You wish to split the document into
        segments of about 1000 tokens, but you prefer to keep paragraphs together
        if this does not increase or decrease the token size by more than 5%.
        This is a convenience wrapper around `segment_fuzzy()`.

    Args:
        segment_size (int): The target size of each segment, in tokens.
        tolerance (Number): see `segment_fuzzy`
        chunker (callable): a one-argument function that cuts the document into
            chunks. If this is present, it is called on the given document.
        tokenizer (callable): a one-argument function that tokenizes each chunk.
        flatten_chunks (bool): if True, undo the effect of the chunker by
            chaining the chunks in each segment, thus each segment consists of
            tokens. This can also be a one-argument function in order to
            customize the un-chunking.

    Example:
        >>> list(segment([['This', 'test', 'is', 'very', 'clear'],
        ...               ['and', 'contains', 'chunks']], 2)) # doctest: +NORMALIZE_WHITESPACE
        [[['This', 'test']],
        [['is', 'very']],
        [['clear'], ['and']],
        [['contains', 'chunks']]]
    """
    if chunker is not None:
        document = chunker(document)
    if tokenizer is not None:
        document = map(tokenizer, document)

    segments = segment_fuzzy(document, segment_size, tolerance)

    if flatten_chunks:
        if not callable(flatten_chunks):
            def flatten_chunks(segment):
                return list(chain.from_iterable(segment))
        segments = map(flatten_chunks, segments)
    if materialize:
        segments = list(segments)

    return segments


def tokenize(doc_txt, expression=regular_expression, lower=True, simple=False):
    """Tokenizes with Unicode Regular Expressions.

    Description:
        With this function you can tokenize a document with a regular expression.
        You also have the ability to commit your own regular expression. The default
        expression is '\p{Letter}+\p{Punctuation}?\p{Letter}+', which means one or
        more letters, followed by one or no punctuation, followed by one or more
        letters. So one letter words won't match.
        In case you want to lower alls tokens, set the argument `lower` to True (it
        is by default).
        If you want a very simple and primitive tokenization, set the argument
        `simple` to True.
        Use the functions `read_from_txt()`, `read_from_tei()` or `read_from_csv()`
        to read your text files.

    Args:
        doc_txt (str): Document as string.
        expression (str): Regular expression to find tokens.
        lower (boolean): If True, lowers all words. Defaults to True.
        simple (boolean): Uses simple regular expression (r'\w+'). Defaults to False.
            If set to True, argument `expression` will be ignored.

    Yields:
        Tokens

    Example:
        >>> list(tokenize("This is one example text."))
        ['this', 'is', 'one', 'example', 'text']
    """
    if lower:
        doc_txt = doc_txt.lower()
    if simple:
        pattern = regex.compile(r'\w+')
    else:
        pattern = regex.compile(expression)
    doc_txt = regex.sub("\.", "", doc_txt)
    doc_txt = regex.sub("‒", " ", doc_txt)
    doc_txt = regex.sub("–", " ", doc_txt)
    doc_txt = regex.sub("—", " ", doc_txt)
    doc_txt = regex.sub("―", " ", doc_txt)
    tokens = pattern.finditer(doc_txt)
    for match in tokens:
        yield match.group()


def filter_pos_tags(doc_csv, pos_tags=['ADJ', 'V', 'NN']):
    """Gets lemmas by selected POS-tags from DARIAH-DKPro-Wrapper output.

    Description:
        With this function you can select certain columns of a CSV file
        generated by `DARIAH-DKPro-Wrapper`_, a tool for natural language processing.
        Use the function `read_from_csv()` to read CSV files.
        .. _DARIAH-DKPro-Wrapper:
            https://github.com/DARIAH-DE/DARIAH-DKPro-Wrapper

    Args:
        doc_csv (DataFrame): DataFrame containing DARIAH-DKPro-Wrapper output.
        pos_tags (list[str]): List of DKPro POS-tags that should be selected.
            Defaults to '['ADJ', 'V', 'NN']'.

    Yields:
        Lemma.

    Example:
        >>> df = pd.DataFrame({'CPOS': ['CARD', 'ADJ', 'NN', 'NN'],
        ...                    'Lemma': ['one', 'more', 'example', 'text']})
        >>> list(filter_pos_tags(df))[0] # doctest: +NORMALIZE_WHITESPACE
        1    more
        2    example
        3    text
        Name: Lemma, dtype: object
    """
    log.info("Accessing %s ...", pos_tags)
    doc_csv = doc_csv[doc_csv['CPOS'].isin(pos_tags)]
    yield doc_csv['Lemma']


def create_sparse_matrix(doc_labels, doc_tokens, type_dictionary, doc_ids):
    """Creates sparse matrix for bag-of-words model.

    Description:
        This function creates a sparse DataFrame containing document and type
        identifier as multiindex and type frequencies as values representing the
        counts of tokens for each token in each document.
        It is also the main function that incorporates the private functions
        `_create_large_counter()` and `_create_sparse_index()``.
        Use the function `get_labels()` for `doc_labels`, `tokenize()` for
        `doc_tokens`, and `create_dictionary()` for `type_dictionary` as well
        as for `doc_ids`.

    Args:
        doc_labels (list[str]): List of doc labels as string.
        doc_tokens (list[str]): List of tokens as string.
        type_dictionary (dict[str]): Dictionary with {token: id}.
        doc_ids (dict[str]): Dictionary with {document label: id}.

    Returns:
        Multiindexed Pandas DataFrame.

    ToDo:
        Test if it's necessary to build sparse_df_filled with int8 zeroes instead of int64.

    Example:
        >>> doc_labels = ['exampletext']
        >>> doc_tokens = [['short', 'example', 'text']]
        >>> type_dictionary = {'short': 1, 'example': 2, 'text': 3}
        >>> doc_ids = {'exampletext': 1}
        >>> create_sparse_matrix(doc_labels, doc_tokens, type_dictionary, doc_ids) # doctest: +NORMALIZE_WHITESPACE
                         0
        doc_id token_id
        1      1         1
               2         1
               3         1
    """
    temp_counter = _create_large_counter(
        doc_labels, doc_tokens, type_dictionary)
    largecounter = {doc_ids[key]: value for key, value in temp_counter.items()}
    sparse_index = _create_sparse_index(largecounter)
    sparse_df_filled = pd.DataFrame(
        np.zeros((len(sparse_index), 1), dtype=int), index=sparse_index)
    index_iterator = sparse_index.groupby(
        sparse_index.get_level_values('doc_id'))

    for doc_id in range(1, len(sparse_index.levels[0]) + 1):
        for token_id in [val[1] for val in index_iterator[doc_id]]:
            sparse_df_filled.set_value(
                (doc_id, token_id), 0, int(largecounter[doc_id][token_id]))
    return sparse_df_filled


def find_stopwords(sparse_bow, id_types, mfw=200):
    """Creates a stopword list.

    Description:
        With this function you can determine most frequent words, also known as
        stopwords. First, you have to translate your corpus into the bag-of-words
        model using the function `create_sparse_matrix()` and create an dictionary
        containing types and identifier using `create_dictionary()`.

    Args:
        docterm_matrix (DataFrame): DataFrame with term and term frequency by document.
        id_types (dict[str]): Dictionary with {token: id}.
        mfw (int): Target size of most frequent words to be considered.

    Returns:
        Most frequent words in a list.

    Example:
        >>> doc_labels = ['exampletext']
        >>> doc_tokens = [['short', 'short', 'example', 'text']]
        >>> id_types = {'short': 1, 'example': 2, 'text': 3}
        >>> doc_ids = {'exampletext': 1}
        >>> sparse_bow = create_sparse_matrix(doc_labels, doc_tokens, id_types, doc_ids)
        >>> find_stopwords(sparse_bow, id_types, 1)
        ['short']
    """
    log.info("Finding stopwords ...")
    type2id = {value: key for key, value in id_types.items()}
    sparse_bow_collapsed = sparse_bow.groupby(
        sparse_bow.index.get_level_values('token_id')).sum()
    sparse_bow_stopwords = sparse_bow_collapsed[0].nlargest(mfw)
    stopwords = [type2id[key]
                 for key in sparse_bow_stopwords.index.get_level_values('token_id')]
    return stopwords


def find_hapax(sparse_bow, id_types):
    """Creates a list with hapax legommena.

    Description:
        With this function you can determine hapax legomena for each document.
        First, you have to translate your corpus into the bag-of-words
        model using the function `create_sparse_matrix()` and create an dictionary
        containing types and identifier using `create_dictionary()`.

    Args:
        sparse_bow (DataFrame): DataFrame with term and term frequency by document.
        id_types (dict[str]): Dictionary with {token: id}.

    Returns:
        Hapax legomena in a list.

    Example:
        >>> doc_labels = ['exampletext']
        >>> doc_tokens = [['short', 'example', 'example', 'text', 'text']]
        >>> id_types = {'short': 1, 'example': 2, 'text': 3}
        >>> doc_ids = {'exampletext': 1}
        >>> sparse_bow = create_sparse_matrix(doc_labels, doc_tokens, id_types, doc_ids)
        >>> find_hapax(sparse_bow, id_types)
        ['short']
    """
    log.info("Finding hapax legomena ...")
    type2id = {value: key for key, value in id_types.items()}
    sparse_bow_collapsed = sparse_bow.groupby(
        sparse_bow.index.get_level_values('token_id')).sum()
    sparse_bow_hapax = sparse_bow_collapsed.loc[sparse_bow_collapsed[0] == 1]
    hapax = [type2id[key]
             for key in sparse_bow_hapax.index.get_level_values('token_id')]
    return hapax


def remove_features(mm, id_types, features):
    """Removes features.

    Description:
        With this function you can clean your corpus from stopwords and hapax
        legomena.
        First, you have to translate your corpus into the bag-of-words
        model using the function `create_sparse_matrix()` and create an dictionary
        containing types and identifier using `create_dictionary()`.

    Args:
        docterm_matrix (DataFrame): DataFrame with term and term frequency by document.
        features (set): Set with features to remove.
        (not included) features (str): Text as iterable. Use `read_from_txt()` to create iterable.

    Returns:
        Clean corpus.

    ToDo:
        Adapt function to work with mm-corpus format.
    """
    log.info("Removing features ...")

    if isinstance(features, set):

        stoplist_applied = [word for word in set(
            id_types.keys()) if word in features]

        clean_term_frequency = mm.drop(
            [id_types[word] for word in stoplist_applied], level="token_id")

    else:

        try:

            features = set(features)

        except:

            log.debug("features must be set or convertible to set")

        stoplist_applied = [word for word in set(
            id_types.keys()) if word in features]

        clean_term_frequency = mm.drop(
            [id_types[word] for word in stoplist_applied], level="token_id")

    total = len(features)

    log.debug("%s features removed.", total)
    return clean_term_frequency


def create_dictionary(list_of_strings):
    """Creates a dictionary of unique strings with identifier.

    Args:
        list_of_strings(list): List of strings.

    Returns:
        Dictionary.

    Example:
        >>> create_dictionary(['example'])
        {'example': 1}
    """
    if all(isinstance(element, list) for element in list_of_strings):
        list_of_strings = {
            string for element in list_of_strings for string in element}
    return {string: id_ for id_, string in enumerate(set(list_of_strings), 1)}


def _create_large_counter(doc_labels, doc_tokens, type_dictionary):
    """create_large_TF_matrix

    Note:
        The main function is create_mm(). This creates a dictionary of dictionaries.
        The first level consitst of key = document label : value = dictionary of counts pairs.
        The second level consists of key = token id : value = count of tokens in document pairs.

    Args:
        doc_labels(list): List of doc labels as string.
        doc_tokens(list): List of tokens as string.
        type_dictionary(dict): Dictionary with key = token : value = id paris.

    Returns:
        Dictionary of document : counter pairs. With counter being token id : count pairs.

    ToDo:
    """

    largecounter = defaultdict(dict)

    for doc, tokens in zip(doc_labels, doc_tokens):

        largecounter[doc] = Counter(
            [type_dictionary[token] for token in tokens])

    return largecounter


def _create_sparse_index(largecounter):
    """create_large_TF_matrix

    Note:
        The main function is create_mm(). This creates a pandas multiindex out of tuples.
        The Multiindex represents document id to token ids relations.

    Args:
        largecounter(dict of counters): Dictionary of document : counter pairs. With counter
                                        being token id : count pairs.

    Returns:
        Pandas Multiindex with document id to token id relations.
    ToDo:
    """

    tuples = []

    for key in range(1, len(largecounter) + 1):

        if len(largecounter[key]) == 0:
            tuples.append((key, 0))

        for value in largecounter[key]:

            tuples.append((key, value))

    sparse_index = pd.MultiIndex.from_tuples(
        tuples, names=["doc_id", "token_id"])

    return sparse_index


def make_doc2bow_list(sparse_bow):
    """Creates doc2bow_list as input for gensim model.get_document_topics(doc2bow_list[idx])

    Note:

    Args:

    Returns:

    ToDo:
    """
    doc2bow_list = []

    for doc in sparse_bow.index.groupby(sparse_bow.index.get_level_values('doc_id')):
        temp = [(token, count) for token, count in zip(
            sparse_bow.loc[doc].index, sparse_bow.loc[doc][0])]
        doc2bow_list.append(temp)

    return doc2bow_list


def make_doc_topic_matrix(model, doc2bow_list, doc2id):
    """Use only for testing purposes, not working properly

    Note:

    Args:

    Returns:

    ToDo: make it work
    """
    df = pd.DataFrame()
    for idx, doc in enumerate(doc2bow_list, 1):
        df[doc2id[idx]] = pd.Series(
            [value[1] for value in model.get_document_topics(doc)])
    return df.fillna(0)


def gensim2dataframe(model):
    """Creates DataFrame out of gensim model (topic keys)

    Note:
        If you want to save Gensim's topic output set log = True

    Args:
        model: Gensim LDA model

    Returns:
        Pandas DataFrame with topics

    ToDo:
        Format input for DataFrame
    """
    num_topics = model.num_topics
    topics_df = pd.DataFrame(index=range(num_topics), columns=range(10))

    topics = model.show_topics(
        num_topics=num_topics, log=False, formatted=False)

    for topic in topics:
        idx = topic[0]
        temp = topic[1]
        topics_df.loc[idx] = temp

    return topics_df


def save_bow_mm(sparse_bow, output_path):
    """Save bag-of-word model as market matrix

    Note:
        Create sparse_bow with create_mm() or take output from remove_features().
        Ouput Path gives name of new local file.

    Args:
        sparse_bow(Pandas DataFrame):  Multiindexed Pandas DataFrame with
                                        document id - token id - count data.
        output_path(str): Path to output file.
    Returns:
        None. Creates file with sparse_bow in market matrix format.

    ToDo:
    """
    num_docs = sparse_bow.index.get_level_values("doc_id").max()
    num_types = sparse_bow.index.get_level_values("token_id").max()
    sum_counts = sparse_bow[0].sum()

    header_string = str(num_docs) + " " + str(num_types) + \
        " " + str(sum_counts) + "\n"

    with open('.'.join([output_path, 'mm']), 'w', encoding="utf-8") as f:
        f.write("%%MatrixMarket matrix coordinate real general\n")
        f.write(header_string)
        sparse_bow.to_csv(f, sep=' ', header=None)